Analysis of cancer omics data: a selective review of statistical techniques

Abstract

Abstract Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data—which has higher dimensionality, weaker signals and more complex distributional properties—is much more challenging. Developments in the literature are often ‘scattered’, with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the ‘overall framework’ of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss ‘special topics’ including interaction analysis, multi-datasets analysis and multi-omics analysis.